Use when auditing a codebase for semantic duplication - functions that do the same thing but have different names or implementations. Especially useful for LLM-generated codebases where new functions are often created rather than reusing existing ones.

Finding Duplicate Intent Functions Overview LLM generated codebases accumulate semantic duplicates: functions that serve the same purpose but were implemented independently. Classical copy paste detectors (jscpd) find syntactic duplicates but miss "same intent, different implementation." This skill uses a two phase approach: classical extraction followed by LLM powered intent clustering. When to Use Codebase has grown organically with multiple contributors (human or LLM) You suspect utility functions have been reimplemented multiple times Before major refactoring to identify consolidation opportunities After jscpd has been run and syntactic duplicates are already handled Quick Reference Phase Tool Model Output 1. Extract scripts/extract functions.sh catalog.json 2. Categorize scripts/categorize prompt.md haiku categorized.json 3. Split scripts/prepare category analysis.sh categories/ .json 4. Detect scripts/find duplicates prompt.md opus duplicates/ .json 5. Report scripts/generate report.sh report.md Process Phase 1: Extract Function Catalog Options: o FILE : Output file (default: stdout) c N : Lines of context to capture (default: 15) t GLOB : File types (default: .ts, .tsx, .js, .jsx ) include tests : Include test files (excluded by default) Test files ( .test. , .spec. , tests / ) are excluded by default since test utilities are less likely to be consolidation candidates. Phase 2: Categorize by Domain Dispatch a haiku subagent using the prompt in scripts/categorize prompt.md . Insert the contents of catalog.json where indicated in the prompt template. Save output as categorized.json . Phase 3: Split into Categories Creates one JSON file per category. Only categories with 3+ functions are worth analyzing. Phase 4: Find Duplicates (Per Category) For each category file in ./categories/ , dispatch an opus subagent using the prompt in scripts/find duplicates prompt.md . Save each output as ./duplicates/{category}.json . Phase 5: Generate Report Produces a prioritized markdown report grouped by confidence level. Phase 6: Human Review Review the report. For HIGH confidence duplicates: 1. Verify the recommended survivor has tests 2. Update callers to use the survivor 3. Delete the duplicates 4. Run tests High Risk Duplicate Zones Focus extraction on these areas first they accumulate duplicates fastest: Zone Common Duplicates utils/ , helpers/ , lib/ General utilities reimplemented Validation code Same checks written multiple ways Error formatting Error to string conversions Path manipulation Joining, resolving, normalizing paths String formatting Case conversion, truncation, escaping Date formatting Same formats implemented repeatedly API response shaping Similar transformations for different endpoints Common Mistakes Extracting too much : Focus on exported functions and public methods. Internal helpers are less likely to be duplicated across files. Skipping the categorization step : Going straight to duplicate detection on the full catalog produces noise. Categories focus the comparison. Using haiku for duplicate detection : Haiku is cost effective for categorization but misses subtle semantic duplicates. Use Opus for the actual duplicate analysis. Consolidating without tests : Before deleting duplicates, ensure the survivor has tests covering all use cases of the deleted functions.

finding-duplicate-functions